A machine-learning approach for automatic grape-bunch detection based on opponent colors

This paper presents a novel and automatic artificial-intelligence (AI) method for grape-bunch detection from RGB images. It mainly consists of a cascade of support vector machine (SVM)-based classifiers that rely on visual contrast-based features that, in turn, are defined according to grape bunch color visual perception. Due to some principles of opponent color theory and proper visual contrast measures, a precise estimate of grape bunches is achieved. Extensive experimental results show that the proposed method is able to accurately segment grapes even in uncontrolled acquisition conditions and with limited computational load. Finally, such an approach requires a very small number of training samples, making it appropriate for onsite and real-time applications that are implementable on smart devices, usable and even set up by winemakers

Inference methods for gravitational wave data analysis

Einstein's publication of the General Theory of Relativity in 1915, and the discovery of a wave-like solution to the field-equations of that theory sparked a century-long quest to detect gravitational waves. These illusive metric disturbances were predicted to ripple-away from some of the most energetic events in the universe, such as supernovae and colliding black holes. The quest was completed in September 2015, with the LIGO observation of a gravitational wave produced by a pair of coalescing black holes, but work to continue detecting and interpreting the signals which are detected by LIGO and its brethren is by no means complete. The age of gravitational wave observation has arrived, and with it the difficulties of interpreting myriad signals, differentiating them from noise, and analysing them in order to gain insight into the astrophysical systems which produced them. This thesis provides overview of the history of the field of gravitational wave science: both in terms of the theoretical principles which frame it, and the attempts to build instruments which could measure them. It then provides a discussion of the morphologies of the signals which are searched for in current detectors' data, and the astrophysical systems which may produce such signals. It is of great importance that the sensitivity of both detectors and the signal analysis techniques which are used is well-understood. A substantial part of the novel work presented in this document discusses the development of a technique for assessing this sensitivity, through a software package called Minke. Knowing the sensitivity of a detector to signals from an astrophysical source allows robust limits to be placed on the rate at which these events occur. These rates can then be used to infer properties of astrophysical systems; this document contains a discussion of a technique which was developed by the author to allow the determination of the geometry of beamed emission from short gamma ray bursts which result from neutron star coalescences. This method finds that at its design sensitivity we expect the advanced LIGO detector to be able to place limits on the opening angle, θ, of the beam within θ ∈ (8.10°,14.95°) under the assumption that all neutron star coalesences produce jets, and that gamma ray bursts occur at an illustrative rate of R = 10 / Gpc³ / year. The most efficient methods for extracting signals from noisy data, such as that produced by gravitational wave detectors, and then analysing these signals, requires robust prior knowledge of the signals' morphologies. The development of a new model for producing gravitational waveforms for coalescing binary black hole systems is discussed in detail in this work. The method which is used, Gaussian process regression, is introduced, with an overview of different methods for implementing models which use the method. The model, named Heron, is itself presented, and comparisons between the waveforms produced by Heron and other models which are currently used in analysis are made. Comparisons between the Heron model and highly accurate numerical relativity waveforms are also shown

Accelerating gravitational-wave inference with machine learning

The future for gravitational-wave astronomy is bright, with improvements for existing ground-based interferometers of the LIGO-Virgo-KAGRA Collaboration (LVK) and new ground- and space-based interferometers planned for the near future. As a result, there will imminently be an abundance of data to analyse from these detectors, which will bring with it the chances to probe new regimes. However, this will also bring with it new challenges to address, such as the volume of data and need for new analysis techniques. Leveraging this data hinges on our ability to determine the characteristics of the sources that produce the observed gravitational-wave signals, and Bayesian inference is the method of choice. The main algorithms that have been used in these analyses are Markov Chain Monte Carlo and Nested Sampling. Each have their own advantages and disadvantages. However, both are computationally expensive when applied to gravitational-wave inference, typically taking of order days to weeks for shorter signals and up to months for longer signals, such as those from binary neutron star mergers. Furthermore, the cost of these analyses increases as additional physics is included, such as higher-order modes, precession and eccentricity. These factors, combined with the previously mentioned increase in data, and therefore number of signals, pose a significant challenge. As such, there is a need for faster and more efficient algorithms for gravitational-wave inference. In this work, we present novel algorithms that serve as drop-in replacements for existing approaches but can accelerate inference by an order of magnitude. Our initial approach is to incorporate machine learning into an existing algorithm, namely nested sampling, with the aim of accelerating it whilst leaving the underlying algorithm unchanged. To this end, we introduce nessai, a nested sampling algorithm that includes a novel method for sampling from the likelihood-constrained prior that leverages normalizing flows, a type of machine learning algorithm. Normalizing flows can approximate the distribution of live points during a nested sampling run, and allow for new points to be drawn from it. They are also flexible and can learn complex correlations, thus eliminating the need to use a random walk to propose new samples. We validate nessai for gravitational-wave inference by analysing a population of simulated binary black holes (BBHs) and demonstrate that it produces statistically consistent results. We also compare nessai to dynesty, the standard nested sampling algorithm used by the LVK, and find that, after some improvements, it is on average ∼ 6 times more efficient and enables inference in time scales of order 10 hours on a single core. We also highlight other advantages of nessai, such as the included diagnostics and simple parallelization of the likelihood evaluation. However, we also find that the rejection sampling step necessary to ensure new samples are distributed according to the prior can be a significant computational bottleneck. We then take the opposite approach and design a custom nested sampling algorithm tailored to normalizing flows, which we call i-nessai. This algorithm is based on importance nested sampling and incorporates elements from existing variants of nested sampling. In contrast to the standard algorithm, samples no longer have to be ordered by increasing likelihood nor distributed according to the prior, thus addressing the aforementioned bottleneck in nessai. Furthermore, the formulation of the evidence allows for it to be updated with batches of samples rather than one-by-one. The algorithm we design is centred around constructing a meta-proposal that approximates the posterior distribution, which is achieved by iteratively adding normalizing flows until a stopping criterion is met. We validate i-nessai on a range of toy test problems which allows us to verify the algorithm is consistent with both nessai and, when available, the analytic results. We then repeat a similar analysis to that performed previously, and analyse a population of simulated BBH signals with i-nessai. The results show that i-nessai produces consistent results, but is up to 3 times more efficient than nessai and more than an order of magnitude more efficient (13 times) than dynesty. We also apply i-nessai to a binary neutron star (BNS) analysis and find that it can yield results in less than 30 minutes whilst only requiring O(106 ) likelihood evaluations. Having developed tools to accelerate parameter estimation, we then apply them to real data from LVK observing runs. We choose to analyse all 11 events from O1 and small selection of events from O2 and O3 and find good agreement between our results and those published by the LVK This demonstrates that nessai can be used to analyse real gravitational-wave data. However, it also highlights aspects that could be improved to further accelerate the algorithm, such as how the orbital phase and multimodal likelihood surfaces are handled. We also show how i-nessai can be applied to real data, but ultimately conclude that further work is required to determine if the settings used are robust. Finally, we consider nessai in the context of next generation ground-based interferometers and highlight some of the challenges such analyses present. As a whole, the algorithms introduced in this work pave the way for faster gravitational wave inference, offering speed-ups of up to an order of magnitude compared to existing approaches. Furthermore, they demonstrate how machine learning can be incorporated into existing analyses to accelerate them, which has the additional benefit of providing drop-in replacements for existing tools

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Measuring health-state utilities for cost-utility analysis

Ph.DDOCTOR OF PHILOSOPH

Quantum enhanced metrology: quantum mechanical correlations and uncertainty relations

The foundational theory of quantum enhanced metrology for parameter estimation is of fundamental importance to the progression of science and technology as the scientific method is built upon empirical evidence, the acquisition of which is entirely reliant on measurement. Quantum mechanical properties can be exploited to yield measurement results to a greater precision (lesser uncertainty) than that which is permitted by classical methods. This has been mathematically demonstrated by the derivation of theoretical bounds which place a fundamental limit on the uncertainty of a measurement. Furthermore, quantum metrology is of immediate interest in the application of quantum technologies since measurement plays a central role. This thesis focuses on the role of quantum correlations and uncertainty relations which govern the precision bounds. We show how correlations can be distributed amongst limited resources in realistic scenarios, as permitted by current experimental capabilities, to achieve higher precision measurements than current approaches. This is extended to the setting of multiparameter estimation in which we demonstrate a more technologically feasible method of correlation distribution than those previously posited which perform as well as, or worse than, our scheme. Furthermore, a quantum metrology protocol is typically comprised of three stages: probe state preparation, sensing and then readout, where the time required for the first and last stages is usually neglected. We consider the more realistic sensing scenario of time being a limited resource which is divided amongst the three stages and demonstrate the most efficient use of this resource. Additionally, we take an information theoretic approach to quantum mechanical uncertainty relations and derive a one-parameter class of uncertainty relations which supplies more information about the quantum mechanical system of interest than conventional uncertainty relations. Finally, we demonstrate how we can use this class of uncertainty relations to reconstruct information of the state of the quantum mechanical system

Learning condition-specific networks

Condition-specific cellular networks are networks of genes and proteins that describe functional interactions among genes occurring under different environmental conditions. These networks provide a systems-level view of how the parts-list (genes and proteins) interact within the cell as it functions under changing environmental conditions and can provide insight into mechanisms of stress response, cellular differentiation and disease susceptibility. The principle challenge, however, is that cellular networks remain unknown for most conditions and must be inferred from activity levels of genes (mRNA levels) under different conditions. This dissertation aims to develop computational approaches for inferring, analyzing and validating cellular networks of genes from expression data. This dissertation first describes an unsupervised machine learning framework for inferring cellular networks using expression data from a single condition. Here cellular networks are represented as undirected probabilistic graphical models and are learned using a novel, data-driven algorithm. Then several approaches are described that can learn networks using data from multiple conditions. These approaches apply to cases where the condition may or may not be known and, therefore, must be inferred as part of the learning problem. For the latter, the condition variable is allowed to influence expression of genes at different levels of granularity: condition variable per gene to a single condition variable for all genes. Results on simulated data suggest that the algorithm performance depends greatly on the size and number of connected components of the union network of all conditions. These algorithms are also applied to microarray data from two yeast populations, quiescent and non-quiescent, isolated from glucose starved cultures. Our results suggest that by sharing information across multiple conditions, better networks can be learned for both conditions, with many more biologically meaningful dependencies, than if networks were learned for these conditions independently. In particular, processes that were shared among both cell populations were involved in response to glucose starvation, whereas the processes specific to individual populations captured characteristics unique to each population. These algorithms were also applied for learning networks across multiple species: yeast (S. cerevisiae) and fly (D. melanogaster). Preliminary analysis suggests that sharing patterns across species is much more complex than across different populations of the same species and basic metabolic processes are shared across the two species. Finally, this dissertation focuses on validation of cellular networks. This validation framework describes scores for measuring how well network learning algorithms capture higher-order dependencies. This framework also introduces a measure for evaluating the entire inferred network structure based on the extent to which similarly functioning genes are close together on the network

Advancing the search for gravitational waves using machine learning

Over 100 years ago Einstein formulated his now famous theory of General Relativity. In his theory he lays out a set of equations which lead to the beginning of a brand-new astronomical field, Gravitational wave (GW) astronomy. The LIGO-Virgo-KAGRA Collaboration (LVK)’s aim is the detection of GW events from some of the most violent and cataclysmic events in the known universe. The LVK detectors are composed of large-scale Michelson Morley interferometers which are able to detect GWs from a range of sources including: binary black holes (BBHs), binary neutron stars (BNSs), neutron star black holes (NSBHs), supernovae and stochastic GWs. Although these GW events release an incredible amount of energy, the amplitudes of the GWs from such events are also incredibly small. The LVK uses sophisticated techniques such as matched filtering and Bayesian inference in order to both detect and infer source parameters from GW events. Although optimal under many circumstances, these standard methods are computationally expensive to use. Given that the expected number of GW detections by the LVK will be of order 100s in the coming years, there is an urgent need for less computationally expensive detection and parameter inference techniques. A possible solution to reducing the computational expense of such techniques is the exciting field of machine learning (ML). In the first chapter of this thesis, GWs are introduced and it is explained how GWs are detected by the LVK. The sources of GWs are given, as well as methodologies for detecting various source types, such as matched filtering. In addition to GW signal detection techniques, the methods for estimating the parameters of detected GW signals is described (i.e. Bayesian inference). In the second chapter several machine learning algorithms are introduced including: perceptrons, convolutional neural networks (CNNs), autoencoders (AEs), variational autoencoders (VAEs) and conditional variational autoencoders (CVAEs). Practical advice on training/data augmentation techniques is also provided to the reader. In the third chapter, a survey on several ML techniques applied a variety of GW problems are shown. In this thesis, various ML and statistical techniques were deployed such as CVAEs and CNNs in two first-of-their-kind proof-of-principle studies. In the fourth chapter it is described how a CNN may be used to match the sensitivity of matched filtering, the standard technique used by the LVK for detecting GWs. It was shown how a CNN may be trained using simulated BBH waveforms buried in Gaussian noise and signals with Gaussian noise alone. Results of the CNN classification predictions were compared to results from matched filtering given the same testing data as the CNN. In the results it was demonstrated through receiver operating characteristics and efficiency curves that the ML approach is able to achieve the same levels of sensitivity as that of matched filtering. It is also shown that the CNN approach is able to generate predictions in low-latency. Given approximately 25000 GW time series, the CNN is able to produce classification predictions for all 25000 in 1s. In the fifth and sixth chapters, it is shown how CVAEs may be used in order to perform Bayesian inference. A CVAE was trained using simulated BBH waveforms in Gaussian noise, as well as the source parameter values of those waveforms. When testing, the CVAE is only supplied the BBH waveform and is able to produce samples from the Bayesian posterior. Results were compared to that of several standard Bayesian samplers used by the LVK including: Dynesty, ptemcee, emcee, and CPnest. It is shown that when properly trained the CVAE method is able to produce Bayesian posteriors which are consistent with other Bayesian samplers. Results are quantified using a variety of figures of merit such as probability-probability (p-p) plots in order to check the 1-dimensional marginalised posteriors from all approaches are self-consistent with the frequentist perspective. The Jensen—Shannon (JS)-divergence was also employed in order to compute the similarity of different posterior distributions from one another, as well as other figures of merit. It was also demonstrated that the CVAE model was able to produce posteriors with 8000 samples in under a second, representing a 6 order of magnitude increase in performance over traditional sampling methods

Holographic studies of thermalization and dissipation in strongly coupled theories

textThis thesis presents a series of studies of thermalization and dissipation in a variety of strongly coupled systems. The main tool for these investigations is the Gauge/Gravity duality, which establishes a correspondence between a d+1-dimensional quantum theory of gravity and a d-dimensional quantum field theory. We study the decay rates of fluctuations around the thermal equilibrium in theories in non-commutative geometry. Rapid thermalization of such fluctuations is found and motivates the conjecture that the phenomena at the black hole horizon is described by non-local physics. In the same type of environment, we analyze the Langevin dynamics of a heavy quark, which undergoes Brownian motion. We find that the late-time behavior of the displacement squared is unaffected by the non-commutativity of the geometry. In a different scenario, we study the correlation functions in theories with quantum critical points. We compute the response of these quantum critical points to a disturbance caused by a massive charged particle and analyze its late time behavior. Finally, we analyze systems far-from-equilibrium as they evolve towards a thermal state. We characterize this evolution for systems with chemical potential by focusing on the ``strong subadditivity" property of their entanglement entropy. This is achieved on the gravity side by using time dependent functions for mass and charge in an AdS-Vaydia metric.Physic